A classification of multitemporal Landsat TM data using principal component analysis and artificial neural network

Abstract

Multitemporal Landsat TM imagery were classified to extract land cover information using principal component analysis (PCA) and backpropagation (BP) algorithm of artificial neural network. Data used are two Landsat TM data of in Jan. 1, 1991 (Data I) and May 9, 1994 (Data II). Twelve bands data were compressed to 4 bands data by the first and second PCA. Approximately 95 percent of the total variance of each Landsat TM data was included resulting from the first and second component analysis. Analyzed data through the PCA were classified by the BP training algorithm of artificial neural network. As a result of classification, it is concluded that this approach will become an attractive and effective method in extracting land cover or land use information using multitemporal Landsat TM data.